Extracellular volume-based scoring system for tracking tumor progression in pancreatic cancer patients receiving intraoperative radiotherapy

Objectives To investigate the value of extracellular volume (ECV) derived from portal-venous phase (PVP) in predicting prognosis in locally advanced pancreatic cancer (LAPC) patients receiving intraoperative radiotherapy (IORT) with initial stable disease (SD) and to construct a risk-scoring system based on ECV and clinical-radiological features. Materials and methods One hundred and three patients with LAPC who received IORT demonstrating SD were enrolled and underwent multiphasic contrast-enhanced CT (CECT) before and after IORT. ECV maps were generated from unenhanced and PVP CT images. Clinical and CT imaging features were analyzed. The independent predictors of progression-free survival (PFS) determined by multivariate Cox regression model were used to construct the risk-scoring system. Time-dependent receiver operating characteristic (ROC) curve analysis and the Kaplan–Meier method were used to evaluate the predictive performance of the scoring system. Results Multivariable analysis revealed that ECV, rim-enhancement, peripancreatic fat infiltration, and carbohydrate antigen 19-9 (CA19-9) response were significant predictors of PFS (all p < 0.05). Time-dependent ROC of the risk-scoring system showed a satisfactory predictive performance for disease progression with area under the curve (AUC) all above 0.70. High-risk patients (risk score ≥ 2) progress significantly faster than low-risk patients (risk score < 2) (p < 0.001). Conclusion ECV derived from PVP of conventional CECT was an independent predictor for progression in LAPC patients assessed as SD after IORT. The scoring system integrating ECV, radiological features, and CA19-9 response can be used as a practical tool for stratifying prognosis in these patients, assisting clinicians in developing an appropriate treatment approach. Critical relevance statement The scoring system integrating ECV fraction, radiological features, and CA19-9 response can track tumor progression in patients with LAPC receiving IORT, aiding clinicians in choosing individual treatment strategies and improving their prognosis. Key Points Predicting the progression of LAPC in patients receiving IORT is important. Our ECV-based scoring system can risk stratifying patients with initial SD. Appropriate prognostication can assist clinicians in developing appropriate treatment approaches. Graphical Abstract


Introduction
Approximately 30% of patients with locally advanced pancreatic cancer (LAPC) die of local progression [1], and only 11% of these patients could survive more than 3 years [2].Therefore, improving the rate of local control and preventing local progression are crucial issues to be addressed [1,3].
Intraoperative radiotherapy (IORT), a precise therapeutic approach that delivers high doses of radiation directly into the tumor while protecting adjacent organs, has demonstrated promising outcomes with improved local disease control rate and potential prognosis in LAPC [4].IORT is recommended as experts' consensus to perform on LAPC patients to relieve symptoms and obtain extra benefits [4,5].Around 50-80% of pancreatic ductal adenocarcinoma (PDAC) patients were assessed as stable disease (SD) based on response evaluation criteria in solid tumor version 1.1 (RECIST v.1.1)after treatment [6].Despite no significant change in tumor diameter in these patients, some of them could achieve major pathologic response and better prognosis [7].Some patients assessed as SD responded well and had longer progression-free survival (PFS), whereas others progressed rapidly [8].Consequently, it is crucial to determine an efficient approach to further stratify the risk of progression in individuals assessed as SD initially after IORT.Many efforts have been made to solve this problem, including clinicopathological features, molecular biomarkers, imaging features, and radiomics [9][10][11].Unfortunately, the potential of these methods is far from sufficiently investigated.
Multi-detector computed tomography is the recommended imaging technique in the evaluation of PDAC, which consists of a dual-phase contrast-enhanced protocol dedicated to the pancreas routinely [12][13][14].The extracellular volume (ECV) fraction, representing the sum of the extravascular extracellular space and the intravascular space, could be estimated by means of contrastenhanced CT (CECT) [15].Recently, it has been revealed that ECV is associated with fibrosis and deposition of an extracellular stromal matrix of the pancreas [15,16].Desmoplastic stroma exhibits an essential role in tumor oncogenesis, proliferation, progression, metastasis, and chemoresistance [16,17].ECV fraction could be used to evaluate pancreatic fibrosis and predict tumor aggressiveness, treatment response, and prognosis in PDAC [15,18].Based on the above, we hypothesize that ECV might become a potential imaging biomarker for stratifying the risk of progression in SD patients.However, until recently, ECV was estimated mainly via the delayed or equilibrium phase of CECT, which prolonged examination time and limited its clinical practicality.Dualenergy CT was also employed in other investigations, but this requires specialized equipment and scanning protocols.Nevertheless, it has not been determined whether the ECV, which is derived from the portal-venous phase (PVP) of conventional CT, could be a more acceptable non-invasive imaging biomarker for prognostic prediction in PDAC.Many studies have shown that clinical factors and CT imaging features, for example, CA19-9, necrosis, and peripancreatic tumor infiltration, were valuable in predicting the prognosis of PDAC [9,12].
A risk-scoring system could serve as a simple and efficient way to evaluate clinicopathological features or prognosis in PDAC [19].Therefore, the purpose of this study was to investigate the potential role of ECV derived from PVP of conventional CT in predicting progression and construct a progression risk-scoring system based on ECV and clinical-radiological features in LAPC patients assessed as SD initially after IORT.

Patients
The institutional review board approved this retrospective study and waived the requirement for informed consent.Between June 2012 and April 2019, we initially enrolled 184 patients with LAPC who received IORT as first-line treatment according to multidisciplinary team discussion at our hospital.LAPC was diagnosed in accordance with National Comprehensive Cancer Network (NCCN) guidelines [14].The inclusion criteria were as follows: (a) without any anti-tumor therapy before IORT; (b) underwent multi-phase CECT examinations before and after IORT; (c) treatment response was initially assessed as SD by RECIST v.1.1 after 4 weeks of IORT; (d) baseline serum CA19-9 > 37 U/mL and CA19-9 available at around 4 weeks after IORT; (e) regular follow-up after IORT.
Eighty-one patients were excluded for certain reasons as listed in Fig. 1.
Finally, 103 patients including 64 males and 39 females (mean age, 58.52 ± 10.09 years) were recruited in this study.The flow chart of the patient enrollment process and study design are depicted in Figs. 1 and S1.

CT technique
All patients underwent a standard multi-phase CECT dedicated to pancreas acquisition in non-enhanced (N), arterial phase (AP), pancreatic parenchymal phase (PPP), and PVP.Iopromide (Ultravist 370, Schering, Berlin, Germany) was administered at a rate of 3.5 mL/sec, with a weight-dependent dose of 1.5 mL/kg.After the contrast agent injection, the average delay times of AP, PPP, and PVP were 25-30 s, 40-50 s, and 65-70 s, respectively.Multiple CT scanners were used because of the retrospective design of this study.The detailed CT scan parameters are listed in Table S1.

ECV fraction analysis
Registration was first performed to alleviate the negative impacts resulting from phase mismatch through an opensourced Python package (ANTsPy, https://github.com/ANTsX/ANTsPy).The registered CT images were processed using an in-house developed program written in Python (version 3.12.0).According to the previous study [18], ECV map can be calculated as the ratio of enhancement of the tumor tissue to the enhancement of the blood pool (abdominal aorta) on PVP-enhanced CT multiplied by the difference of 1-minus the hematocrit value, using the formula (1) by a pixel-wise method employing N and PVP CT images as input: where ΔHU tumor and ΔHU aorta represent the difference in the CT attenuation of the tumor and aorta between the unenhanced and PVPs.All images were analyzed by two abdominal radiologists (Z.T and D.L with 8 and 6 years of experience in abdominal imaging) who were blinded to the clinical information.Using 3D slicer (version 5.2.2; www.slicer.org), an open-source image processing software, the entire tumor was included in the freehand volume of interest (VOI) on PVP images slice by slice, carefully avoiding adjacent vasculature, necrosis, and cysts, since these components may affect the accuracy of the estimation of tumor ECV values.Then the VOIs were automatically copied to ECV maps to extract the mean values of ECV.The average of the measurements from the two radiologists was used for further analysis.Interobserver agreement on the segmentations was evaluated by the Dice similarity coefficient (DSC).A DSC greater than 0.80 was considered satisfactory reproducibility.Discrepancies in the segmentations (DSC < 0.50) were resolved by consensus.

Radiological feature analysis
Two abdominal radiologists (Y.Z and W.C with 10 and 5 years of abdominal imaging experience, respectively) who were aware of the diagnosis of PDAC but blinded to the clinical details independently reviewed the CT images.Seven radiological features before IORT were evaluated, including tumor location, necrosis, rim-enhancement, peripancreatic fat infiltration, pancreatic duct dilatation, atrophic upstream pancreatic parenchyma, and suspicious lymph node metastasis.The definitions of these features were proposed by the Society of Abdominal Radiology dedicated to PDAC and previous studies (Table S2) [20][21][22].Detailed steps to ensure consistency during evaluation were provided in Appendix E1.Inter-reader agreements were evaluated after independent image analysis, and all discrepancies were resolved during the second reading.

Clinical data
Clinical data were collected from the electronic medical records, including age, sex, body mass index (BMI), adjuvant therapy, jaundice, clinical American Joint Committee on Cancer (AJCC) TNM stage [14], serum CA19-9 level at baseline and after IORT (if the patients had obstructive jaundice, resampled after biliary drainage), carcinoembryonic antigen (CEA), carbohydrate antigen 242 (CA 242), hematocrit, bilirubin, albumin, D-dimer, fibrinogen, glucose, and transferrin at baseline, which were included for the reasons listed in the Table S3.
The CA19-9 response was defined as an over 50% reduction from the baseline level or decreasing to the normal range (below 37 U/mL) after IORT.The CA19-9 change was calculated using the formula (2):

IORT and adjuvant therapy
The IORT procedure and sequential adjuvant treatment regimen were determined by a standardized protocol reported in experts' consensus [4] and established by the abdominal oncology multidisciplinary team at our institution.All patients received chemotherapy or chemoradiotherapy after IORT.Detailed information is provided in Appendix E2, Table S4, and Fig. S2.

Follow-up
All patients were regularly followed up through outpatient clinic visits.Physical examination and laboratory tests were performed monthly.Imaging examination was performed every 3 months.The end of the follow-up date was June 30, 2019.PFS was defined as the time from IORT to any event of local tumor progression, distal metastasis, or death associated with the tumor.
The Cox proportional hazards model was conducted to identify independent predictors for PFS.Referring to Sullivan's method [23], a risk-scoring system was developed depending on the β coefficients of the Cox regression model.Time-dependent receiver operating characteristic curve (ROC) analysis was applied to evaluate the predictive ability of the risk-scoring system.In addition, calibration plots with 1000 bootstrap resamples were used to assess the accuracy of survival rate prediction.An outcome-based optimal cut-off value for the total risk score was determined using a maximally selected rank statistics algorithm (Maxstat package) in R statistical software (R, version 4.3.0;R Foundation for Statistical Computing, Vienna, Austria) [24].PFS was assessed using Kaplan-Meier method and compared by log-rank test.A two-sided p value < 0.05 was considered statistically significant.All statistical analyses were conducted using R software.

Patient characteristics
The demographic and clinical characteristics of the patients are presented in Table 1.The time interval from baseline CT to IORT was 10.0 days (range, 8.0-14.0days) (Fig. S3).

CT imaging features assessment
The CT imaging features (radiological features and quantitative parameters) are summarized in Table 1.The DSC of the VOI segmentation was 0.84 ± 0.06.Kappa and Interobserver agreement analyses showed good or excellent agreement for radiological features (0.70-1.00) (Table S5) and CT quantification parameters (0.82-0.87) (Table S6).

Development of risk-scoring system for PFS
A risk-scoring system based on the independent risk factors for PFS was developed, with total scores ranging from −2 to 5 (Table 3).Time-dependent area under curves (AUCs) within 18 months were employed to assess the accuracy of the risk-scoring system, which demonstrated good performance with AUCs all above 0.70 (Fig. 3A).The 6-month calibration curve for prediction risk and actual observation of progression agreed satisfactory (Fig. 3B).

Risk stratification for predicting progression and post hoc subgroup analysis
For LAPC patients, the probability of PFS at 6 months, dropped as the total risk score increased (Fig. 4A).Furthermore, a higher score was associated with poorer outcomes for LAPC (Fig. 4B).A cut-off value of 2 points was determined to stratify patients into high-risk (≥ 2 points) (n = 31) and low-risk (< 2 points) (n = 72) for progression (Fig. 4C).Patients with low-risk progressed slower than those with high-risk after IORT, with median PFS of 8.90 months (95% CI: 7.48-10.31months) and 4.40 months (95% CI: 3.55-5.25months), respectively (p < 0.001) (Table 4 and Fig. 4D).The 3-, 6-and 12-month PFS rates in the low-risk group were 93.1%, 72.2%, and 25.0%, while 81.0%, 12.9%, and 0.6%, respectively, in the high-risk group (all p < 0.001) (Table 4).The distribution of risk scores and representative cases for different risk groups were shown in Fig. 5.The predictive ability for progression as estimated using the risk-scoring system at various time points was summarized in Table 5 and Fig. S4.Notably, the risk-scoring system demonstrated the highest efficacy in predicting ≥ 30% probability of 1-year PFS, with an accuracy of 94.4%.Post-hoc subgroup analysis showed significant prognostic differences (all p < 0.001) between low-and highrisk patients in patients either receiving adjuvant chemotherapy or chemoradiotherapy postoperatively (Fig. 4E, F).

Serial tumor response after IORT
A serial tumor progression diagram in patients assessed as SD initially of the low-risk and high-risk groups stratified by risk-scoring system was demonstrated in Fig. 6.Finally, 18 and 3 patients in the low-risk and high-risk groups achieved no disease progression after 12 months, respectively.

Discussion
In the present study, the ECV fraction derived from PVP was proven to be an independent risk factor for  In this study, 103 patients included 64 males and 39 females, with a mean age of 58.52 ± 10.09 years, which is similar to the previous study [25].Demographic breakdown in our study aligns with known characteristics of LAPC patients.
Given the fact that PDAC is rich in stromal components, the treatment-induced desmoplastic stromal response, edema, and inflammatory response after radiotherapy can lead to no apparent change in lesion diameter or even pseudo-progression [26].As a result, evaluating and reflecting true therapeutic benefits, and stratifying the prognosis based on conventional morphological changes in SD patients are challenging.Various risk stratification methods have already been reported to overcome the drawbacks of RECIST in PDAC [9][10][11]27].Yang et al proposed a radiomics signature to predict outcomes in LAPC patients with SD [11].Another study revealed that lower circulating tumor DNA before treatment was associated with SD [27].However, the clinical application of these methods was limited due to their complexity, poor repeatability, time-consuming nature, or high cost.Consequently, it is crucial to investigate a new simple, and effective approach for progression risk stratification in SD patients to facilitate precise therapeutic decisions.The four predictors identified in our risk-scoring system were easily accessible and routinely used in the clinic.Our study showed the high value of ECV derived from PVP indicated a low risk of progression after IORT.This could be explained by the fact that PDAC contains a lot of tumor stroma and a poor blood supply [7].ECV has been shown to be associated with fibrosis, desmoplastic stroma, and tissue elasticity [18].A reduced ECV value indicates vascular deficiency and severe necrosis induced by hypoxia.Furthermore, tumor hypoxia contributes to the sensitivity of radiotherapy, which results in radiotherapy resistance and a poor prognosis [28,29].In contrast, a high ECV reflects an enlarged extracellular space that might be composed of abundant micro-vessels at the Fig. 3 Predictive performance of risk-scoring system.A The time-dependent area under the ROC curve (AUC) plot with a 95% confidence interval shows the risk-scoring system in predicting PFS for each follow-up time point within 18 months.B Calibration plot for the overall survival prediction at the 6-month.PFS, progression-free survival; ROC, receiver operating characteristic Fig. 4 The relationship between risk score and progression.A The disease progression pattern plot demonstrates a decreasing probability of progression-free survival along with an increasing risk score at 6 months for LAPC patients.B The patients were divided into two groups according to the threshold of the cut-off value of the risk score.Green represents the low-risk group.Orange represents the high-risk group.The dashed-gray line represents the cut-off value.C The waterfall plot shows the distribution of the total risk score of individuals and the predicted progression risk of LAPC patients after IORT according to the scoring system.The dashed-gray line represents the cut-off value.D Kaplan-Meier curves show the overall survival of patients in the high-risk and low-risk groups.E, F Kaplan-Meier PFS curves in post hoc subgroup analyses according to postoperative adjuvant chemotherapy or chemoradiotherapy.IORT, Intraoperative radiotherapy; LAPC, locally advanced pancreatic cancer; PFS, progression-free survival histopathological level, potentially resulting in a low level of hypoxia and thus more sensitivity to radiotherapy.The expansion of extracellular space facilitates the penetration and distribution of chemotherapy drugs, allowing adjuvant chemotherapy to kill tumor cells more effectively.All the above reasons might contribute to the relatively good prognosis in patients with high ECV.In this study, we adopted ECV fraction derived from PVP rather than other delayed times (3, 5, or 10 min) [18,30], which effectively reduced the examination time and radiation doses.Moreover, there were also articles that calculated ECV late-arterial-phase [31].At present, there is no consensus on the delay time for calculating ECV.PVP, one of the conventional contrast-enhanced phases in pancreatic protocol CT, is routinely used in clinical practice and it might be a potential alternative to the equilibrium phase for calculating ECV [14].Meanwhile, no attempt has been made to generate ECV fraction by PVP previously.Although Noid calculated ECV fraction based on latearterial-phase, they only analyzed the association between ECV and CA19-9 and did not directly investigate its correlation with treatment response [31].Our study confirmed that ECV in PVP was an independent predictor of prognosis.
Two CT radiological features, rim enhancement, and peripancreatic fat infiltration, were demonstrated as predictors for the progression of LAPC in our study, as previously reported [21,32].It has been revealed that the hypo-attenuation areas in rim-enhancement were significantly associated with high histological grade, rapid proliferation, few residual acini, and severe necrosis [21,33], which might result in tumor hypoxia, consequently leading to resistance to radiotherapy.From this perspective, the presence of rim-enhancement might indicate insensitivity to radiotherapy [28,29].Recently, some studies have found that peripancreatic fat infiltration, which reflects the extent of tumor invasion to  surrounding tissues, is associated with a low R0 resection rate and poor prognosis [12,32].These are consistent with the findings of our study.Serum CA19-9 is a widely used tumor marker, treatment response indicator, and prognostic predictor in PDAC [14,34,35].CA19-9 positively correlated with tumor load, and a decline over 20%, 50%, or to normality after treatment was reported to be an indicator of favorable prognosis [34,36].A recent clinical trial utilized a 50% reduction in CA19-9 levels after treatment as one of the criteria for evaluating response to treatment [35], and Newhook et al had previously demonstrated that a decrease of CA19-9 levels over 50% or to normal implies improved overall survival (OS) [34,36].What's more, the normal range of serum CA19-9 was less than 37 U/mL according to the NCCN guideline for PDAC [14].However, as the literature reported, there exist CA19-9 non-secretors with very low CA19-9 levels (≤ 2 U/mL) or normal ranges (< 37 U/mL) [37,38].To avoid difficult or inaccurate assessment of patients with normal CA19-9 levels who had a relatively small decrease in CA19-9 or remained within the normal range, we only included patients with CA19-9 > 37 U/ mL.Therefore, our study adopted the criteria of a decrease of over 50% or to a normal range for CA19-9 responders, which confirmed a longer PFS time compared with non-responders.
Our study had several limitations.First, the sample size was relatively small, and the patients were from a single institution and a retrospective cohort.Thus, a prospective cohort from multi-centers is required to validate the results of this study.Second, multiple CT scanners were used due to the retrospective study.A standardized scanning parameter and contrast injection protocols were followed for adopted for all patients in this study, which might minimize the variations between devices and ensure comparable imaging results.Third, we only included LAPC patients with CA19-9 over 37 U/mL, so the scoring system is not applicable to all pancreatic cancer patients, such as Lewis negative PDAC.Fourth, only PFS, but not OS was analyzed in the present study.Fifth, the consistency or correlation between PVP-based ECV and equilibrium-based ECV was not investigated because of the retrospective nature of this study.Prospective research should be performed to evaluate whether PVP-based ECV could replace equilibriumbased ECV in the future.Last, CT-based ECVs are not suitable for patients with CECT contraindications, such as iodine allergy.Whether magnetic resonance imaging-based ECV could replace CT-based ECV remains to be investigated.Therefore, further research is needed to address the above concerns.
In conclusion, ECV derived from PVP can be used in predicting the progression risk in LAPC patients

Fig. 2
Fig. 2 Forest plots for the multivariable Cox regression analysis for PFS.The vertical dotted line represents hazard ratios equal to 1.0.Numbers inside the red circles are HRs, and horizontal lines on either side of the circles represent 95% CIs.CA 19-9, carbohydrate antigen 19-9; CI, confidence interval; ECV, tumor extracellular volume; PFS, progression-free survival

Fig. 5
Fig. 5 Distribution of risk scores and examples of groups at different risks of progression.A Histogram of the distribution of risk score is shown on the left.The dashed line represents the cut-off value.The right side of the figure shows examples of CT imaging features in different risk groups.From left to right, the images of non-enhanced (B, E), PVP with VOI of tumor at the lower left (C, F), and ECV map (D, G) are shown sequentially.B-D A 64-year-old man was diagnosed as LAPC with a 4.3 cm tumor at the tail of the pancreas before IORT and was assessed as SD after IORT.The patient presented rimenhancement and peripancreatic fat infiltration (arrow, C) with an ECV fraction of 15.9%, and CA19-9 non-response.He was predicted at high risk of progression with a total risk score of 5 points.Finally, this patient progressed due to liver metastases at 3.5 months.E-G A 61-year-old man with LAPC at the body of the pancreas before IORT.In this case, rim-enhancement and peripancreatic fat infiltration were absent (F), while the ECV fraction was 23.7% and CA19-9 was grouped as a response.Based on a total risk score of −2 points, this patient was classified as low risk of progression.After 16.5 months of IORT, the patient had local progression.CA 19-9, carbohydrate antigen 19-9; ECV, extracellular volume; IORT, intraoperative radiotherapy; LAPC, locally advanced pancreatic cancer; SD, stable disease; VOI, volume of interest; PVP, portal-venous phase initially assessed as SD after IORT.The risk-scoring system integrating ECV, CT radiological features, and CA19-9 response could serve as an efficient and practical tool for prognosis stratification in LAPC patients with SD.It could assist RECIST v.1.1 to further identify SD patients who might be sensitive to and benefit from IORT accurately, aiding clinicians in choosing individual treatment strategies, preventing tumor progression, and improving the prognosis of patients with LAPC after IORT.

Table 1
Baseline characteristics and CT imaging features of 103 patients and univariate Cox analysis for PFS

Table 1 continued
Variables with p < 0.05 in univariate analysis were highlighted in bold and applied to multivariate analysis using a stepwise Cox proportional hazards regression model AJCC American Joint Committee on Cancer, ALB albumin, BMI body mass index, CA19-9 carbohydrate antigen 19-9, CEA carcinoembryonic antigen, CA 242 cancer antigen 242, CI confidence interval, TBil total bilirubin, DBil direct bilirubin, ECV tumor extracellular volume, HR hazard ratio, PFS progression-free survival a Data are reported as mean ± standard deviation or median with interquartile range in parentheses for continuous variables and number (%) of patients for categoric variables

Table 2
Multivariate Cox proportional hazard analysis for PFS of LAPC patients The ellipsis indicates p value is not significant and should be excluded from the multivariate Cox model Variables with p < 0.05 in multivariate Cox hazards regression analysis were highlighted in bold PFS progression-free survival, HR hazard ratio, CI confidence interval, CEA carcinoembryonic antigen, CA19-9 carbohydrate antigen 19-9, ECV tumor PFS in LAPC patients assessed with SD, which could further stratify the risk of progression in these patients with satisfactory prognostic predictive performance.Our scoring system could serve as a complement to the RECIST v.1.1 criteria to identify LAPC patients with SD who would develop progress at risk after IORT, allowing clinicians to adopt appropriate treatment strategies and improve the prognosis.

Table 3
The risk-scoring system for the prognosis prediction in LAPC patients assessed as SD CA19-9 carbohydrate antigen 19-9, ECV tumor extracellular volume, SD stable disease

Table 4
Analysis for PFS between predicted high-or low-risk group a n = 67, 93.1% n = 25, 81.0% < 0.001 PFS rate in 6 months a n = 52, 72.2% n = 4, 12.9% < 0.001 PFS rate in 12 months a n = 18, 25.0% n = 3, 9.6% < 0.001 PFS progression-free survival, CI confidence interval # p Value was calculated by log-rank tests a Data are reported as the number and percentage of patients, and p value was calculated by χ2 tests

Table 5
Prediction performance for PFS of risk scoreData are percentages with 95% CIs in parentheses and numbers of observations in brackets PFS progression-free survival, CI confidence interval, PPV positive predictive value, NPV negative predictive value